Semantic Relation Classification by Bi-directional LSTM Architecture
نویسندگان
چکیده
Semantic relation extraction is a meaningful task in NLP that could provide some helpful information and semantic relation classification attracts many people to research it. This paper mainly introduces a Bi-direction LSTM (long short-term memory) deep neutral network and the parameter of embedding layer, and this network can solve the problem of over-fitting. And then according to the text of dataset, I propose a new idea to optimize the input structure and sequence padding.
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تاریخ انتشار 2017